Effects of Borrower-Defined Conditions in the Online Peer-to-Peer Lending Market

Qiu, Jiaxian; Lin, Zhangxi; Luo, Bo

doi:10.1007/978-3-642-29873-8_16

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…For investors interested in socially conscious investing, P2P lending offers the possibility of supporting the attempts of individuals to break free from high-rate debt, assist persons engaged in occupations or activities that are deemed moral and positive to the community, and avoid investment in persons employed in industries deemed immoral or detrimental to community [5].…”

Section: Discussionmentioning

confidence: 99%

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Why Do So Many Online P2P Lending Platform in China Turn Bad: Scenario Analysis Viewpoint

Jing¹

2019

dtcse

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P2P lending companies eager to succeed in the online P2P lending platform are encouraged to imitate the strategies and business models of successful businesses. Yet they often ignore the lessons from online P2P lending platforms that failed with similar business models. Our analysis of the online P2P lending platforms turn to bad has identified several causes of failure, and the associated risks, when competing in the P2P lending. This research aims to highlight some of the most important aspects which can significantly reduce the inherent risks and can lead to sustainable growth in the online P2P lending market.

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Section: Discussionmentioning

confidence: 99%

“…The online P2P lending platform is shown in Figure 4. This system is intended to provide quasi real-time and dynamic credit services for online financial services [4] [5]. To innovate existing P2P lending business models with support by advanced credit theories.…”

Section: Discussionmentioning

confidence: 99%

Why Do So Many Online P2P Lending Platform in China Turn Bad: Scenario Analysis Viewpoint

Jing¹

2019

dtcse

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“…However, humanized personal data and loan reasons have an impact on the success of capital loans [21]. Qiu et al further revealed that in addition to personal information and social capital, the loan amount, the highest acceptable interest rate, and other variables such as the loan period set by the borrower also have a significant effect on the success or failure of the fund [31].…”

Section: Online Loan Credit Risk Factormentioning

confidence: 99%

Credit Risk Management of P2P Network Lending

Ding

et al. 2021

Teh. vjesn.

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This article first studies the literature of P2P online loans, including online loans, credit risk factors and models, and summarizes the current status of P2P and credit risk assessment management in China. Based on the loan data of domestic P2P lending platforms, this paper conducts an empirical study on credit risk assessment. This study uses random forest importance assessment and logistic regression classification for credit risk assessment to identify loan targets with higher probability of default and improve overall loan quality. This research used 10,930 loan data, based on 26 fields, and finally selected 20 model variables to participate in credit risk quantification through feature structure and feature analysis. The final modelling test results show that the model screening accuracy rate is 73.3%, indicating that this model has a good performance in the credit risk quantification of borrowers.

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